A CNN-BiLSTM Bearing Fault Diagnosis Model Based on an Improved Grey Wolf Optimization Algorithm.
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| Title: | A CNN-BiLSTM Bearing Fault Diagnosis Model Based on an Improved Grey Wolf Optimization Algorithm. |
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| Authors: | Deng, Xitong1 dxt@stu.ustl.edu.cn, Wang, Yukun1 wyk410@ustl.edu.cn, Meng, Qingyao2 mengqingyao@qdec.edu.cn |
| Source: | Engineering Letters. May2026, Vol. 34 Issue 5, p1874-1891. 18p. |
| Subjects: | Fault diagnosis, Grey Wolf Optimizer algorithm, Hilbert-Huang transform, Convolutional neural networks, Long short-term memory, Mechanical vibration research, Deep learning |
| Abstract: | As industrial machinery continues to progress, ensuring the reliable identification of bearing abnormalities has become a key topic in mechanical engineering. To address the need for fast and precise fault assessment, this work develops a CNN-BiLSTM-based diagnostic approach enhanced by an Improved Grey Wolf Optimization strategy (HSGWO). In the proposed framework, the HSGWO-tuned ICEEMDAN-PE method is first applied to condense and preprocess vibration measurements. A combined CNN and BiLSTM network is then constructed to perform fault classification and condition forecasting, taking advantage of the CNN's strong feature-extraction capability and the BiLSTM's effectiveness in modeling temporal dependencies. Moreover, HSGWO is used to automatically search for suitable network hyperparameters--such as hidden-layer size, learning rate, and L2 penalty--allowing the model to better capture complex signal patterns and improving its robustness. Experimental findings indicate that the designed model consistently surpasses traditional machine-learning algorithms and several advanced deep-learning baselines, showing notable gains in accuracy, precision, recall, and F1 score. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193453942 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A CNN-BiLSTM Bearing Fault Diagnosis Model Based on an Improved Grey Wolf Optimization Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Deng%2C+Xitong%22">Deng, Xitong</searchLink><relatesTo>1</relatesTo><i> dxt@stu.ustl.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yukun%22">Wang, Yukun</searchLink><relatesTo>1</relatesTo><i> wyk410@ustl.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Meng%2C+Qingyao%22">Meng, Qingyao</searchLink><relatesTo>2</relatesTo><i> mengqingyao@qdec.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. May2026, Vol. 34 Issue 5, p1874-1891. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Hilbert-Huang+transform%22">Hilbert-Huang transform</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+vibration+research%22">Mechanical vibration research</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As industrial machinery continues to progress, ensuring the reliable identification of bearing abnormalities has become a key topic in mechanical engineering. To address the need for fast and precise fault assessment, this work develops a CNN-BiLSTM-based diagnostic approach enhanced by an Improved Grey Wolf Optimization strategy (HSGWO). In the proposed framework, the HSGWO-tuned ICEEMDAN-PE method is first applied to condense and preprocess vibration measurements. A combined CNN and BiLSTM network is then constructed to perform fault classification and condition forecasting, taking advantage of the CNN's strong feature-extraction capability and the BiLSTM's effectiveness in modeling temporal dependencies. Moreover, HSGWO is used to automatically search for suitable network hyperparameters--such as hidden-layer size, learning rate, and L2 penalty--allowing the model to better capture complex signal patterns and improving its robustness. Experimental findings indicate that the designed model consistently surpasses traditional machine-learning algorithms and several advanced deep-learning baselines, showing notable gains in accuracy, precision, recall, and F1 score. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1874 Subjects: – SubjectFull: Fault diagnosis Type: general – SubjectFull: Grey Wolf Optimizer algorithm Type: general – SubjectFull: Hilbert-Huang transform Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Mechanical vibration research Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: A CNN-BiLSTM Bearing Fault Diagnosis Model Based on an Improved Grey Wolf Optimization Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Deng, Xitong – PersonEntity: Name: NameFull: Wang, Yukun – PersonEntity: Name: NameFull: Meng, Qingyao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 5 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
